A case-based evolutionary model for defect classification of printed circuit board images

In this research, a case-based evolutionary identification model is developed for PCB defect classification problems. Image understanding is the first and foremost step in the inspection of printed circuit boards. This paper presents a two-phase method for the segmentation of printed circuit board (PCB) images. In the first phase, a set of defect images of several existing basic patterns are stored to form a concept space. In the second phase, a new pattern is evolutionally grabbed using some primitive operators generated by calculating the relative position of several similar cases in the concept space. The case-based reasoning system relies on the software agents derived from past experience within the domain database to determine what feature is required to deliver new patterns in satisfying user’s requirements. Experimental results show that the proposed approach is very effective in identifying the defect patterns.